Horizon University SCDT Building LAN Redesign: A Top-Down Approach to Network Modernization

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Introduction to Network Development Life Cycles and the SCDT LAN Challenge

Campus networks are the lifeblood of modern colleges and universities, powering everything from lectures to advanced research. Horizon University, established in 2001, boasts a sprawling campus—forty-five acres, eight buildings—all dedicated to top-tier learning and research . Six of these buildings house classrooms, while two are reserved for research and administration . At its heart sits the School of Computing and Digital Technologies, but a recent review of its network uncovered some grim realities . Think sluggish data transfers, constant traffic jams, dropped connections, and capacity that buckles under pressure during peak hours . These aren't just minor annoyances; they hobble professors and students alike, particularly those immersed in cloud computing, cybersecurity, or IoT projects that crave a fast, unwavering internet connection .

A robust network strategy and lifecycle management are essential for environments like this . Cisco lays out a continuous, six-stage process for enterprise infrastructure: Prepare, Plan, Design, Implement, Operate, and Optimize . The initial Prepare stage lays the foundation, defining the network strategy and a high-level architecture . Then comes Plan, where the current state is compared against desired outcomes to nail down tasks, responsibilities, timelines, and resource allocation . Following that detailed planning, the Design stage sketches out the precise technical specifications—the actual blueprint . After these critical initial steps, the network is Implemented, ideally without disrupting live services, then Operated for daily management, and finally Optimized using feedback to refine performance and address lingering issues .

These early lifecycle phases are crucial for getting things right; they’re about understanding what users actually need and getting a firm grip on the existing network . Pinpointing user needs means diving deep into network applications, services, business goals, technical objectives, and any organizational or technical constraints . Assessing the current network involves gathering documentation and interviewing stakeholders to establish a performance baseline . While conventional network methodologies differentiate between various deployment perspectives, the provided sources don't specifically compare top-down versus bottom-up network design approaches.

For Horizon University's School of Computing and Digital Technologies, a meticulous requirements analysis and a well-structured design are key to resolving today’s network headaches and paving the way for future ambitions . After extensive discussions with university leadership, faculty, IT staff, and students, a full network overhaul for the school was greenlit . The vision? A network delivering lightning-fast, dependable connections everywhere—labs, offices, research hubs, and student zones . Beyond mere speed, the new infrastructure must significantly enhance basic performance, bolster security, accommodate a surge in connected devices, and provide scalable capacity to support emerging courses and the university's long-term expansion .

Network Design Methodologies: Top-Down vs. Bottom-Up

Network design methods differ fundamentally in how they approach the Open Systems Interconnection (OSI) reference model . A bottom-up approach starts planning at the OSI model's lower layers. It focuses on the physical layer first, immediately picking network hardware, media, protocols, and bandwidth . A top-down design, by contrast, begins at the upper layers. It targets the application layer, sessions, and data transport before deciding on lower-layer physical elements like routers, switches, and cabling . This difference in structure determines whether the network infrastructure shapes how things operate, or if operational needs dictate the infrastructure itself .

The bottom-up method is mainly about quickly building a working infrastructure. It aims to support whatever future applications or organizational needs might arise . Since it operates within an existing setup and often uses patterns from past projects, it offers an advantage for rapid, solid deployment when customers need fast results . However, this rush to connect physically brings significant technical drawbacks. Prioritizing hardware and network models before fully grasping organizational needs means the bottom-up model might end up with a network that doesn't quite fit, a one-size-fits-all solution . Designers often guess how applications will behave, only to find out later that crucial requirements were missed. This leads to severe performance issues, unexpected scalability failures, or paying too much for unnecessary capacity . The sources consulted point to this gap between hardware choices and business needs as a major cause of design failure .

Conversely, the top-down method puts an analysis of organizational needs first, before any technology is chosen . By starting the design at the application layer, engineers can explore organizational structures and understand the end users and systems that will use network services . This business-focused framework systematically aligns technical plans with core business objectives, service agreements, and application needs, while keeping the door open for long-term growth . It's an iterative process. Engineers gain a broad, clear grasp of what customers need before fine-tuning protocol behavior, scalability targets, and physical specs . The main downside is the upfront effort: extensive planning, company-wide collaboration, and thorough needs analysis take a lot of time. This can delay hardware purchases and physical setup .

When comparing both methods, the top-down approach is clearly better for complex company networks. It incorporates specific customer requirements and lays out a clear, comprehensive network plan . For Horizon University's School of Computing and Digital Technologies (SCDT) LAN redesign, the current infrastructure needs an upgrade to fix ongoing problems like slow data transfers, traffic jams during busy times, and dropped connections. While the provided sources don't detail the SCDT building's specific performance data or departmental figures, buying switches or upgrading links without examining the underlying traffic patterns could just repeat past design flaws . A structured top-down method ensures that applications, network services, limitations, and business drivers are fully understood before new equipment is installed. This protects the network's long-term performance, capacity, and efficiency .

Justification for a Top-Down Design Approach for SCDT

Network design choices really determine if campus infrastructure upgrades will work well, bounce back from problems, and actually help the business. When you're engineering a campus network, you need a structured way to think about it, especially if the original setup was based on old tech that just doesn't cut it anymore . To fix those core architectural weak spots, network planners look at two main ways of doing things: top-down and bottom-up.

The bottom-up method starts by picking network tech and hardware first. This is risky because the basic infrastructure might not support what the business and its applications actually need, leading to big implementation problems . On the other hand, the top-down design method organizes the engineering work by breaking down tasks, keeping the design focused, and figuring out technical solutions from a business-first viewpoint . The sources we looked at don't give us the specifics on Horizon University's School of Computing and Digital Technologies (SCDT)—like its exact operational history or building details—nor do they offer hard data on the site's reported issues: slow data speeds, network jams during busy times, and spotty connections. Still, what we know from established design literature shows that tackling these kinds of complicated operational situations demands a top-down strategy. This makes sure that what the organization wants to achieve drives every part of the physical and logical setup .

Why pick the top-down way? It’s all about putting requirements in the right order. Unlike bottom-up thinking, the top-down method grabs customer needs much better. It paints a clear, big-picture view of what the network deployment should look like . This approach sets up a step-by-step workflow: it first looks at the core business goals and what the organization needs. Then, it moves on to defining exactly what the applications require, sets the specifications for the target infrastructure, and includes ways to monitor it all for ongoing improvements . In schools and companies today, where new digital projects have to support special services, what the business needs directly shapes IT and networking plans . Big goals like keeping operations running, being flexible, saving money, controlling access, and meeting rules all need matching network efforts. These include making networks and services reliable, designing them to be adaptable and quick to respond, consolidating and virtualizing the network, and ensuring privacy from one end to the other .

Going with a top-down strategy fits with established lifecycle plans, like Cisco's PPDIOO method. It covers the Prepare, Plan, Design, Implement, Operate, and Optimize stages . PPDIOO manages the whole lifecycle. It cuts down on the total cost of ownership, boosts network availability, lets organizations quickly adapt to changing operational demands, and speeds up access to critical services and applications . By starting the process with identifying customer needs, designers capture business goals, technical objectives, user limits, technical roadblocks, applications, and services *before* they even look at the current network or pick physical hardware .

Using this structured method stops you from buying hardware too early that won't actually fix the deeper, systemic problems. By focusing on strategic operational goals before committing to physical devices, the top-down framework makes sure the resulting infrastructure keeps working, can scale up, stays available, performs well, is easy to manage, and is efficient at every level of operation . Picking the top-down approach offers a structured, business-focused path. It goes after the root causes of architectural limits instead of just swapping out parts, which ensures the institution's long-term health and solid service delivery .

Defining Quantitative Assumptions and Business Goals for SCDT

Horizon University, founded in 2001, sits on about 45 acres in Malaysia, with eight buildings dedicated to its academic life . Six buildings are for teaching and learning; two others house administration and research . Inside this setup, the School of Computing and Digital Technologies (SCDT) urgently needs a new local area network (LAN). An internal review found major problems: slow data transfers, network jams, spotty connections, and not enough speed when everyone's online . These issues cripple academic work, especially for faculty and students doing technical projects in cloud computing, networking, cybersecurity, and IoT. For them, a solid, fast connection isn't optional; it's mandatory . Because good planning needs solid numbers to back it up, the project brief lets students make educated guesses about user numbers, network devices, departments, labs, bandwidth needs, IP addresses, and other connected systems in the SCDT building . The available information doesn't give exact figures, so the team has to systematically assume these details during the analysis and design stage .

The main goals for the SCDT LAN redesign are keeping research going, making the infrastructure expandable, and getting a good return on investment (ROI) . Research universities depend on faculty, students, partners, and global networks all accessing and sharing data collaboratively . In today's research institutions, cyber resilience is key to ensuring scientific discovery keeps moving forward, even when things go wrong operationally . To make research continuity strong, you need a resilient, high-performance environment built with a clear plan . This means identifying which projects and data are most important, controlling access with zero trust principles, using immutability and versioning, and having tested recovery plans ready for research continuity . Instead of just hoping for instant recovery, institutions focus on confirmed recovery times to cut down service interruptions, protect key assets, and support ongoing experiments . Therefore, the SCDT LAN upgrade must protect intensive research projects from local network failures .

The network must also be able to scale up. Horizon University management expects the new network architecture to handle future growth, boost basic performance, strengthen security, and provide enough capacity for new technologies and more connected devices . The underlying infrastructure needs to keep pace with academic research by supporting demanding workloads and offering flexible burst capabilities. This includes integrating with the cloud for shared processing while securing core data . A good return on investment hinges directly on fixing the current bandwidth problems and peak congestion that slow down work . By tackling insufficient peak network capacity and fixing intermittent outages in teaching labs, offices, and research areas, the proposed redesign will protect long-term educational investments, improve efficiency, and cut down on maintenance costs from outdated network setups .

Technical Goals: Scalability, High Availability, and QoS

The School of Computing and Digital Technologies (SCDT) local area network is being redesigned. The goal? Reliable, high-speed connectivity. Plus, better network performance overall, stronger security, room for future growth, and support for new tech alongside more connected devices . Advanced curricula—think cloud computing, cybersecurity, IoT projects—have serious demands. Meeting the operational demands of advanced curricula—specifically cloud computing, networking, cybersecurity, and Internet of Things (IoT) projects—necessitates establishing precise technical goals that encompass scalability, multidimensional high availability, and quality of service (QoS) .

Campus networks must scale to handle organizational growth over time . Bandwidth needs on modern campus networks can easily double several times during their lifespan. That means designing architectures to aggregate traffic from 10 Gbps Ethernet up to 25, 40, or even 100 Gbps capacities . And at the network edge? New devices like smart lighting, cameras, and wireless access points are popping up everywhere. Modern network designs need to support Power over Ethernet (PoE) delivering up to 90W per port for these gadgets . Access layer switches must also keep the PoE flowing even when the switch reboots or gets a software update. This maintains continuity for critical endpoint devices .

High availability means the network must run constantly and be resilient . Campus-wide availability is built on three main failure domains: network resiliency, device resiliency, and operational resiliency . Network resiliency guards against physical transmission issues—bad transceivers, faulty cables, or fiber cuts . Device resiliency keeps things running when a node fails due to software glitches or unresponsive modules . Operational resiliency ensures full availability during planned maintenance, using features like In-Service Software Upgrade (ISSU) .

To achieve predictable convergence, high availability in the distribution layer relies on dual equal-cost paths. These connect the distribution layer to the core and the access layer to the distribution layer . This physical redundancy lets failover depend on detecting hardware link failures, not software timers. The result? Fast, predictable recovery if a link or node goes down . To avoid losing connectivity at the subnet boundary, default gateway redundancy is built across redundant distribution systems. This uses protocols like GLBP, HSRP, or VRRP . Implementing these means you can pull out an individual distribution node without interrupting end-user access to the default gateway. Properly tuned HSRP or GLBP timers can achieve 900 ms convergence during link or node failures .

Beyond structural availability, QoS design principles are essential for managing traffic across campus segments . Network resiliency should be explicitly considered within QoS design frameworks, ensuring that failure states do not compromise traffic handling policies . Low latency is also a critical performance factor for campus networks. It's a key metric for latency-sensitive tasks like unified communications applications . The sources didn't detail specific QoS queue configurations, Class of Service parameters, or latency thresholds for the SCDT building.

Technical Goals: Network Segmentation, Redundancy, and Low Latency

Redesigning the local area network for Horizon University's School of Computing and Digital Technologies (SCDT) demands clear technical goals. We need to tackle intermittent connectivity, peak congestion, and slow data transfers plaguing specialized labs, offices, and research spaces . To ensure high performance, easy maintenance, and scalable operations, campus networks really ought to use a hierarchical, modular topology . A key technical aim involves implementing network segmentation via virtual local area networks (VLANs) and advanced virtualization architectures .

Emerging workloads like the Internet of Things (IoT) require systematic segmentation and virtualization. Technologies like Cisco's Software-Defined Access (SD-Access) help scale services, expand access policies, and keep traffic domains isolated . In today's fabric architectures, virtual networks run on a physical underlay, creating macro-segmentation through virtual routing and forwarding (VRF) instances. This isolates control and data planes without exposing end-user subnets directly to the underlay . Network designs focused on enterprise modernization also incorporate security policy management, data center fabrics, and enterprise network fabrics to handle the increasing computational demands of academia and research .

Network redundancy is absolutely critical. It's how we eliminate single points of failure and ensure high availability across the campus infrastructure . The core layer should be a high-speed Layer 3 switching environment. This boosts scalability by minimizing neighbor meshing, optimizes bandwidth use, and speeds up convergence after link or node outages .

In traditional multi-tier designs, the distribution layer acts as the boundary between Layer 2 and Layer 3 domains, connecting upwards to the campus core with routed uplinks . High availability at this boundary depends on redundant point-to-point connections and default gateway redundancy. Think protocols like Hot Standby Router Protocol (HSRP), Gateway Load Balancing Protocol (GLBP), or Virtual Router Redundancy Protocol (VRRP), which can achieve deterministic sub-second convergence around 900 ms .

For Layer 2 access architectures with potential failure points, redundant supervisors using Stateful Switchover (SSO) offer the best resilience. This limits disruptions to just 1 to 3 seconds, much better than a prolonged outage . Alternatively, a Layer 3 routed access model for the physical underlay sidesteps older loop avoidance methods like Spanning Tree Protocol (STP). It delivers fast, predictable routing convergence for access edge switches .

Achieving low latency and predictable throughput is essential for supporting intensive cloud computing, networking, and distributed computational research in academic settings . Much like modern regional networks that use resilient models for high-speed, low-latency connectivity in collaborative cluster computing and AI simulation research, the SCDT infrastructure must handle heavy transport demands affordably and manageably .

Designing the core layer with dedicated Layer 3 switching and dual equal-cost routed paths allows for rapid failover. Hardware link failure detection triggers this, yielding convergence times of 60 to 200 milliseconds for protocols like OSPF and EIGRP . Restricting broadcast domains and enforcing smaller bandwidth segments through disciplined Layer 3 boundaries cuts down on unmanaged broadcast traffic. This further reduces transmission delay and queuing latency across user and lab segments . By combining structured segmentation, dual-path hardware redundancy, and optimized Layer 3 routing fabrics, this proposed LAN design promises resilient, low-latency communication. It meets current teaching needs and is ready for future expansion .

Network Architectural Models: Three-Tier Hierarchical

Campus network designs need structure. They must meet operational demands for availability, performance, maintainability, and scalability . The Cisco three-tier hierarchical network model offers a framework. It breaks down complex, flat networks into smaller, more manageable units . Instead of an unstructured topology, this hierarchical approach establishes three distinct levels: access, distribution (or aggregation), and core . Each level gets specific jobs. This lets designers pick hardware, software, and switching features suited for each tier . Separating functions this way boosts overall network performance, simplifies management, ensures predictable scalability, and builds in redundancy .

The access layer is where the network begins. It connects local and remote workgroups to endpoints, workstations, servers, and wireless access points . Switches here focus on port density for end devices, balancing cost per port . In typical campus setups, access switches usually act as Layer 2 devices. In standard campus implementations, access layer switches often operate as Layer 2 devices handling port connectivity, virtual local area network demarcation, and initial traffic handoff . By keeping endpoint connections and basic delivery mechanisms in the access tier, traffic flows orderly before heading into higher network paths .

Just above the access switches sits the distribution layer. It's often called the aggregation layer. This is the connectivity crossroads and the boundary between the access layer and the core backbone . This layer handles policy-based connections, boundary enforcement, access control filtering, and Quality of Service management . For campuses spanning multiple buildings or floors, distribution devices usually use Layer 3 switches. They route packets between different VLANs and subnets . This tier acts as the line between Layer 2 broadcast domains and Layer 3 routing domains. It gathers uplinks from access devices and sends traffic towards the core. Dynamic routing protocols are employed here; this boosts the network's overall capacity .

The core layer is the high-speed campus backbone. It’s built for optimal, highly redundant, and fast packet transport. It connects distributed aggregation blocks, building networks, and wide area gateways . Core devices are high-performance routers or Layer 3 switches. They're designed for high forwarding volumes, often with fast interfaces like 10 Gbps or 100 Gbps Ethernet . The core design stays simple. It avoids packet-filtering bottlenecks. This ensures quick convergence, high availability, and easy network expansion as campus needs grow .

Network Architectural Models: Collapsed Core and Spine-Leaf

Enterprise campus networks need structure. Enterprise campus environments require structured network designs to transition from complex, flat topologies into segmented, manageable tiers . Cisco’s classic hierarchical model splits network duties across three distinct layers: the core, the distribution, and the access layer . The core layer is all about super-fast routing and smooth transport between locations. It’s built tough to bounce back from any internal failures . Then, the distribution layer acts as a policy checkpoint between the access and core tiers, often where Layer 2 switching meets Layer 3 routing . Finally, the access layer hooks up workgroups and end-users to the network .

For smaller to mid-sized campuses, a full three-tier setup can mean too much hardware . Network designers get around this by using a two-tier model, often called a collapsed core-distribution architecture . Here, core transport and distribution functions merge into one layer, physically or logically, instead of being separate boxes . Depending on the campus size, how many devices are connected, and if wireless is the main way users get online, combining the core and distribution into a collapsed design is a common move .

Today’s institutions are increasingly adopting advanced architectural frameworks to handle heavy research, massive data growth, and high-performance demands . Modern network modernization, for example, leans on core engineering principles like Enterprise Network Fabrics, Data Center Fabrics, Security Policy Management, High Availability and Redundancy, and Scalability and Flexibility . Within data centers, fabrics are specifically deployed to support high-throughput, low-latency, and resilient computing operations .

Campus networks often compare collapsed core hierarchies with leaf-and-spine fabric designs. However, the available sources don't offer performance benchmarks, protocol details, or direct comparisons for Spine-Leaf architectures beyond noting the need for high-throughput, low-latency Data Center Fabrics . The evidence we found also lacks specific design details, measurements, or lab setups for Horizon University or its School of Computing and Digital Technologies building. Because of this, we have to rely on general campus architectural guidelines. These suggest collapsed core designs offer simpler policy control and routing for moderate-sized networks , while dedicated network and data center fabrics are best for large-scale, high-throughput connections .

Campus Network Design Best Practices and Evolution

Campus network architectures have evolved constantly to meet the demands of data-heavy research, growing institutions, and modern education. Back in the day, places like the University of South Florida upgraded their basic setups from 10 Megabit shared Ethernet to Fast Ethernet, with a Gigabit Ethernet backbone. This was to handle multimedia traffic and high-bandwidth research, like Internet2 .

A common network model for medium-sized higher education environments emphasizes core reliability, full functionality, adherence to standards, scalability, and ongoing evolution. This allows for new advances while keeping critical services running . Generally, a standard campus model can handle anywhere from 100 to 100,000 devices, spread across a single building's floors or hundreds of acres . To keep up with traffic demands at these scales, network topologies define both the physical and logical structures. These dictate device placement and routing to optimize traffic flow, isolate problems, simplify troubleshooting, and support future growth .

Modern university campus upgrades view the network as essential infrastructure for continuous services. At the University of Glasgow, their multi-phase Network Programme is upgrading core infrastructure for uninterrupted operations. They're delivering high-capacity, low-latency connections across campuses to improve the user experience for digital learning, teaching, and research . Meanwhile, initiatives like the one at UW–Madison are tackling the limits of older systems. They're specifically overcoming Spanning Tree Protocol and hardware address limitations by implementing modern fabric architectures and strong high availability. This supports hybrid education, administrative changes, and the growth of data science . Designing these systems needs a modular, hierarchical topology. This limits broadcast domains, separates bandwidth, and provides redundant paths for users to reach central switching resources . Industry standard frameworks for higher education deployments include virtual overlays, like Layer 2 and core aggregation Virtual Extensible LAN solutions .

Physical infrastructure standards and advanced backbone engineering form the bedrock of high-performance campus environments. Established infrastructure specs require dedicated telecom rooms on every floor, with specific placement, space, and environmental requirements . To keep wireless networks performing well in new and renovated areas, full coverage must be planned from the start. This means specifying wall-mounted wireless access points positioned vertically below ceilings, no higher than 2.4 meters, along with telecom outlets. Dual telecom outlets are needed where wireless is a must . At the backbone layer, institutional programs like the University of Michigan's are redesigning campus infrastructures to offer 100 Gigabit connectivity per building, universal WiFi 6E coverage, and specialized support for data-intensive research . Their design principles include diverse cable routing between sites, fully meshed core routing, redundant core pairs with N+1 power protection, and re-architected fabrics. These support operational efficiency and advanced security controls without sacrificing throughput .

Network Design and Simulation Tools: Cisco Packet Tracer

Network design and evaluation rely heavily on software simulation and emulation platforms. These tools model architectures before physical deployment. Cisco Packet Tracer, a proprietary network simulator from Cisco Networking Academy, implements a limited number of Cisco devices . It's mainly an educational tool for novices, focusing solely on Cisco hardware . The platform offers accessible features for configuring basic protocols and practicing cyber defense principles visually . Cisco Packet Tracer is free for Cisco Networking Academy members, providing an easy entry point for academic and institutional instruction .

In practice, Cisco Packet Tracer differs procedurally from hypervisor-driven or image-based emulators like GNS3. Building a network topology in Packet Tracer means selecting and interconnecting devices directly on its canvas . Emulators like GNS3, however, require engineers to first acquire, download, and load real operating system images before connecting devices . Once configured—whether for routing protocols like OSPFv2, addressing schemes, or first-hop redundancy protocols like HSRP—both environments show comparable capabilities. Interconnections, IPv4 addressing, protocol support, and packet delivery execution are similar .

Packet Tracer's main operational advantage is its minimal host computational overhead. Performance evaluations show it uses about 20% of host CPU and roughly 200 MB of RAM . Emulation platforms like GNS3 demand much more from hardware, consuming around 90% of the CPU and 400 MB of RAM per simulated device . This efficiency allows rapid drafting, validation, and testing of basic network topologies on standard workstations, no dedicated virtualization servers needed.

Still, Cisco Packet Tracer has technical limits for complex, heterogeneous enterprise designs. It's a simulator, not an emulator running genuine OS binaries, so it only supports an abstracted, limited subset of Cisco hardware and proprietary implementations . Testing advanced features—like multi-vendor device integration, granular real-time payload inspection with external packet analyzers, or executing real-world multi-stage cyberattack scenarios—goes beyond Packet Tracer's basic protocol engine . Thus, while Packet Tracer is a highly resource-efficient tool for initial protocol validation and foundational network design, broader enterprise-grade infrastructure testing usually requires pairing it with more extensive emulation platforms .

Network Design and Simulation Tools: GNS3 and EVE-NG

Evaluating advanced network modeling platforms is crucial for validating campus architectures before they’re physically built. GNS3, or Graphical Network Simulator 3, is a free, open-source network simulator and emulator. It lets network engineers and researchers run actual networking software images on virtual hardware, building realistic virtualized networks . GNS3 works as both a simulator and emulator. It runs real Cisco IOS image files on virtualized hardware, emulates physical gear with Dynamips, and supports actual operating system images from many network device vendors . Beyond Cisco, GNS3 is completely free and supports over 20 network vendors. It allows real-time network simulation without dedicated physical hardware, while still letting you connect virtual topologies directly to existing physical equipment .

GNS3 offers significant depth for advanced network validation, protocol testing, and security evaluation. Compared to simpler simulators like Cisco Packet Tracer, GNS3 provides more sophisticated functionality. It runs authentic operating systems instead of simulated feature abstractions, making it ideal for complex engineering tasks . Security professionals can recreate cyberattack scenarios and study traffic behavior using its integrated Wireshark network analyzer. This allows for direct packet inspection and analysis during active simulated attacks . For enterprise environments or rigorous proof-of-concept testing where precise behavioral results are essential, GNS3 comes strongly recommended . However, these capabilities come with a significant computational overhead and administrative setup. Unlike platforms where devices can be selected and interconnected immediately, GNS3 requires downloading and loading operating system images for equipment . In comparative benchmarks against Packet Tracer—which uses about 20% CPU and 200 MB RAM—GNS3 demands roughly 90% CPU and 400 MB RAM per device. It also requires software image licensing, depending on the platform used .

EVE-NG offers an alternative high-performance network emulation platform. It shares many operational characteristics with GNS3 but brings distinct architectural capabilities . EVE-NG is described as a multi-vendor network simulator and emulator, facilitating dynamic virtualization to recreate authentic work environments . Much like GNS3, EVE-NG boasts extensive hardware support and enables the modeling and deployment of complex network topologies using multi-vendor network equipment images .

The main technical difference in EVE-NG lies in its platform architecture and how it's accessed. EVE-NG is built on a cloud-based, clientless model, employing a client-server architecture . This framework supports multi-user operation. It allows multiple engineers or students to access virtualized laboratory topologies simultaneously, without needing local software installations on their own computers . As a result, while GNS3 shines in local workstation deployments and offers deep Wireshark-integrated packet inspection for specific cyberattack and protocol scenarios, EVE-NG provides a multi-user collaborative framework perfect for educational and institutional lab settings . Both platforms are powerful emulation tools. They go beyond basic simulation software by running authentic vendor firmware images, delivering the accuracy needed to model complex enterprise networks .

Network Design and Simulation Tools: SolarWinds and Toolset Recommendation

Keeping tabs on network infrastructure requires software that can meticulously document everything, actively discover devices, and keep a close eye on performance. SolarWinds offers tools for businesses; their aim is to map out physical and logical networks and keep tabs on live operations. Specifically, SolarWinds Network Topology Mapper uses Simple Network Management Protocol (SNMP) polling to automatically scan the network . It then generates interactive maps showing how devices connect and how those links fit into larger network segments . By comparing discovery results over time, the software updates diagrams to reflect what's actually happening, tackling the common problem of documentation not matching the real network . This lets network engineers check topologies against live connections, catch unexpected changes, and produce reports that fit into their wider documentation systems .

Beyond mapping, businesses often need constant operational oversight. SolarWinds Network Performance Monitor focuses on tracking faults, performance, and availability across different vendors' equipment . This tool provides hop-by-hop analysis using NetPath and alerts that understand dependencies and topology, helping to pinpoint failures in complex setups . A key point for operations, though: licensing limits its use. SolarWinds Network Performance Monitor is fully functional for just 30 days . The provided information doesn't mention specific costs, renewal prices, or academic deals beyond this trial period.

When combined with virtual emulation platforms, tools like GNS3 can be integrated into design and testing workflows. GNS3 simulates networks in real-time without needing physical gear. It lets engineers test equipment from over 20 vendors and connect virtual setups directly to their existing physical networks . This is great for proving concepts, designing safely in a virtual space, and troubleshooting before going live . SolarWinds Network Performance Monitor can then track and confirm how these GNS3 virtual networks perform during testing .

When recommending a combined toolset, institutions must weigh automated live discovery against simulation and lab modeling. SolarWinds Network Topology Mapper is ideal for documentation where teams need live-discovered maps to validate changes and conduct thorough audits . For constant live fault and performance monitoring, SolarWinds Network Performance Monitor offers hop-by-hop analysis and dependency tracking, but remember its 30-day limit . Meanwhile, virtual emulators like GNS3 provide a complementary space for testing multi-vendor protocols and validating concepts . The provided evidence doesn't detail performance metrics, bandwidth benchmarks, or specific lab outcomes for the Horizon University School of Computing and Digital Technologies (SCDT) building. Still, using SolarWinds Network Topology Mapper for automatic discovery with virtualized testbeds creates a workflow based on confirmed connectivity and solid design validation .

Open questions

— What specific quantitative assumptions regarding user numbers, device counts, and bandwidth needs can be made for the SCDT building?
— What is the precise budget allocated for the SCDT LAN redesign, and how will ROI be measured?
— What are the specific security requirements and policies that need to be implemented in the redesigned SCDT network?
— What are the current network device models and configurations in the SCDT building that need to be replaced or integrated?
— What are the specific emerging technologies that the SCDT network needs to support beyond current known requirements?